SoroushVahidi/scidocs huggingface dataset:
Big Data and the Internet of Things
(2016)

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to “improve” their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.
Sensorless Direct Field-Oriented Control of Three-Phase Induction Motor Drives for Low-Cost Applications
(2006)

A sensorless direct rotor field-oriented control (SDRFOC) scheme of three-phase induction motors for low-cost applications is presented in this paper. The SDRFOC algorithm is based on a sensorless closed-loop rotor flux observer whose main advantages are simplicity and robustness to motor parameter detuning. The whole algorithm has been developed and implemented on a low-cost fixed-point digital signal processor controller. Experimental results are presented for a 0.5-kW induction motor drive for a primary vacuum pump used in industry applications.
Entity Extraction: From Unstructured Text to DBpedia RDF triples
(2012)

In this paper, we describe an end-to-end system that automatically extracts RDF triples describing entity relations and properties from unstructured text. This system is based on a pipeline of text processing modules that includes a semantic parser and a coreference solver. By using coreference chains, we group entity actions and properties described in different sentences and convert them into entity triples. We applied our system to over 114,000 Wikipedia articles and we could extract more than 1,000,000 triples. Using an ontology-mapping system that we bootstrapped using existing DBpedia triples, we mapped 189,000 extracted triples onto the DBpedia namespace. These extracted entities are available online in the N-Triple format1.
Fuzzy Identity Based Encryption
(2004)

We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, ω, to decrypt a ciphertext encrypted with an identity, ω′, if and only if the identities ω and ω′ are close to each other as measured by the “set overlap” distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term “attribute-based encryption”. In this paper we present two constructions of Fuzzy IBE schemes. Our constructions can be viewed as an Identity-Based Encryption of a message under several attributes that compose a (fuzzy) identity. Our IBE schemes are both error-tolerant and secure against collusion attacks. Additionally, our basic construction does not use random oracles. We prove the security of our schemes under the Selective-ID security model.
A review of ontologies for describing scholarly and scientific documents
(2014)

Several ontologies have been created in the last years for the semantic annotation of scholarly publications and scientific documents. This rich variety of ontologies makes it difficult for those willing to annotate their documents to know which ones they should select for such activity. This paper presents a classification and description of these state-of-the-art ontologies, together with the rationale behind the different approaches. Finally, we provide an example of how some of these ontologies can be used for the annotation of a scientific document.
ROC curve equivalence using the Kolmogorov-Smirnov test
(2013)

This paper describes a simple, non-parametric and generic test of the equivalence of Receiver Operating Characteristic (ROC) curves based on a modified Kolmogorov-Smirnov (KS) test. The test is described in relation to the commonly used techniques such as the Area Under the ROC curve (AUC) and the Neyman-Pearson method. We first review how the KS test is used to test the null hypotheses that the class labels predicted by a classifier are no better than random. We then propose an interval mapping technique that allows us to use two KS tests to test the null hypothesis that two classifiers have ROC curves that are equivalent. We demonstrate that this test discriminates different ROC curves both when one curve dominates another and when the curves cross and so are not discriminated by AUC. The interval mapping technique is then used to demonstrate that, although AUC has its limitations, it can be a model-independent and coherent measure of classifier performance.
Using virtual data effects to stabilize pilot run neural network modeling
(2013)

Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive, Artificial Neural Networks are widely utilized to extract management knowledge from acquired data for its non-linear properties; however, getting as large a number of training data as needed is the fundamental assumption. This is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize virtual sample generation techniques and the corresponding data effects to stabilize the prediction model. This research derives from using extreme value theory to estimate the domain range of a small data set, which is used for virtual sample production to fill the information gaps of sparse data. Further, for the virtual samples, a fuzzy-based data effect calculation system is developed to determine the comprehensive importance of each datum. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.
Faster Transit Routing by Hyper Partitioning
(2017)

We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm [16]. Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm. 1998 ACM Subject Classification G.2.2 Graph Theory
Multiagent systems and agent-based modeling and simulation
(2017)

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s). GECCO ’17 Companion, July 15-19, 2017, Berlin, Germany ACM 978-1-4503-4939-0/17/07. http://dx.doi.org/10.1145/3067695.3067723 INSTITUTO DE INFORMÁTICA UFRGS Multiagent Systems and Agent-based Modeling and Simulation
Development of small-sized pixel structures for high-resolution CMOS image sensors
(2017)

We present our studies on small-sized pixel structures for high-resolution CMOS image sensors. To minimize the number of pixel components, single-transistor pixel and 2T pixel architecture were proposed. To deal with crosstalk between pixels, MOS capacitor deep trench isolation (CDTI) was integrated. CDTI-integrated pixel allows better achievements in dark current and full-well capacity in comparison with the configuration integrating oxide-filled deep trench isolation (DTI). To improve quantum efficiency (QE) and minimize optical crosstalk, back-side illumination (BSI) was developed. Also, vertical photodiode was proposed to maximize its charge-collection region. To take advantages of these structures/technologies, we developed two pixel options (P-type and N-type) combining CDTI or DTI, BSI and vertical photodiode. All the presented pixel structures were designed in 1.4µm-pitch sensor arrays, fabricated and tested.
A Context-aware Time Model for Web Search
(2016)

In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.
UIMA Ruta: Rapid development of rule-based information extraction applications
(2016)

Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freelyavailable system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.
Arabic Diacritization with Gated Recurrent Unit
(2018)

Arabic and similar languages require the use of diacritics in order to determine the necessary parameters to pronounce and identify every part of the speech correctly. Therefore, when it comes to perform Natural Language Processing (NLP) over Arabic, diacritization is a crucial step. In this paper we use a gated recurrent unit network as a language-independent framework for Arabic diacritization. The end-to-end approach allows to use exclusively vocalized text to train the system without using external resources. Evaluation is performed versus the state-of-the-art literature results. We demonstrate that we achieve state-of-the-art results and enhance the learning process by scoring better performance in the training and testing timing.
Syntactic Simplification and Text Cohesion
(2006)

Syntactic simplification is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simplification is to make text easier to comprehend for human readers, or process by programs. In this thesis, I describe how syntactic simplification can be achieved using shallow robust analysis, a small set of hand-crafted simplification rules and a detailed analysis of the discourse-level aspects of syntactically rewriting text. I offer a treatment of relative clauses, apposition, coordination and subordination. I present novel techniques for relative clause and appositive attachment. I argue that these attachment decisions are not purely syntactic. My approaches rely on a shallow discourse model and on animacy information obtained from a lexical knowledge base. I also show how clause and appositive boundaries can be determined reliably using a decision procedure based on local context, represented by part-of-speech tags and noun chunks. I then formalise the interactions that take place between syntax and discourse during the simplification process. This is important because the usefulness of syntactic simplification in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. I describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive-relations during syntactic simplification. In order to perform syntactic simplification, I have had to address various natural language processing problems, including clause and appositive identification and attachment, pronoun resolution and referring-expression generation. I evaluate my approaches to solving each problem individually, and also present a holistic evaluation of my syntactic simplification system.
CellNet: Network Biology Applied to Stem Cell Engineering
(2014)

Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.
Physiological Computing: Interfacing with the Human Nervous System
(2010)

This chapter describes the physiological computing paradigm where electrophysiological changes from the human nervous system are used to interface with a computer system in real time. Physiological computing systems are categorized into five categories: muscle interfaces, brain-computer interfaces, biofeedback, biocybernetic adaptation and ambulatory monitoring. The differences and similarities of each system are described. The chapter also discusses a number of fundamental issues for the design of physiological computing system, these include: the inference between physiology and behaviour, how the system represents behaviour, the concept of the biocybernetic control loop and ethical issues.
Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills
(2015)

Parental scaffolding is an important mechanism utilized by infants during their development. Infants, for example, pay stronger attention to the features of objects highlighted by parents and learn the way of manipulating an object while being supported by parents. Parents are known to make modifications in infant-directed actions, i.e. use “motionese”. Motionese is characterized by higher range and simplicity of motion, more pauses between motion segments, higher repetitiveness of demonstration, and more frequent social signals to an infant. In this paper, we extend our previously developed affordances framework to enable the robot to benefit from parental scaffolding and motionese. First, we present our results on how parental scaffolding can be used to guide the robot and modify robot’s crude action execution to speed up learning of complex actions such as grasping. For this purpose, we realize the interactive nature of a human caregiver-infant skill transfer scenario on the robot. During reach and grasp attempts, the movement of the robot hand is modified by the human caregiver’s physical interaction to enable successful grasping. Next, we discuss how parental scaffolding can be used in speeding up imitation learning. The system describes how our robot, by using previously learned affordance prediction mechanisms, can go beyond simple goal-level imitation and become a better imitator using infant-directed modifications of parents.
Sentence Extraction by tf/idf and Position Weighting from Newspaper Articles
(2002)

Recently lots of researchers are focusing their interests on the development of summarization systems from large volume sources combined with knowledge acquisition techniques such as infor mation extraction text mining or information re trieval Some of these techniques are implemented according to the speci c knowledge in the domain or the genre from the source document In this pa per we will discuss Japanese Newspaper Domain Knowledge in order to make a summary My sys tem is implemented with the sentence extraction approach and weighting strategy to mine from a number of documents
N ov 2 01 8 Adaptive Task Allocation for Mobile Edge Learning
(2018)

This paper aims to establish a new optimization paradigm for implementing realistic distributed learning algorithms, with performance guarantees, on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadratically-constrained integer linear problem. Being an NP-hard problem, the paper relaxes it into a non-convex problem over real variables. We thus proposed two solutions based on deriving analytical upper bounds of the optimal solution of this relaxed problem using Lagrangian analysis and KKT conditions, and the use of suggest-and-improve starting from equal batch allocation, respectively. The merits of these proposed solutions are exhibited by comparing their performances to both numerical approaches and the equal task allocation approach.
Distributed energy balanced routing for wireless sensor networks
(2009)

Most routing algorithms for sensor networks focus on finding energy efficient paths to prolong the lifetime of sensor networks. As a result, the power of sensors on efficient paths depletes quickly, and consequently sensor networks become incapable of monitoring events from some parts of their target areas. In many sensor network applications, the events that must be tracked occur at random locations and have non-deterministic generation patterns. Therefore, ideally, routing algorithms should consider not only energy efficiency, but also the amount of energy remaining in each sensor, thus avoiding non-functioning sensors due to early power depletion. This paper introduces a new metric, energy cost, devised to consider a balance of sensors’ remaining energies, as well as energy efficiency. This metric gives rise to the design of the distributed energy balanced routing (DEBR) algorithm devised to balance the data traffic of sensor networks in a decentralized manner and consequently prolong the lifetime of the networks. DEBR is scalable in the number of sensors and also robust to the variations in the dynamics of event generation. We demonstrate the effectiveness of the proposed algorithm by comparing three existing routing algorithms: direct communication approach, minimum transmission energy, and self-organized routing and find that energy balance should be considered to extend lifetime of sensor network and increase robustness of sensor network for diverse event generation patterns. 2009 Elsevier Ltd. All rights reserved.
Learning Ensembles of Convolutional Neural Networks
(2014)

Ensemble learning is a method for generating multiple versions of a predictor network and using them to get an aggregated prediction. Given a learning set Ω consists of data {(yn, ~xn), n = 1, ..., N} where y is the class label and ~x is the inputing feature, we train a predictor φ(~x,Ω). With different initialization, we obtain a series of predictors {φk}. Our object is to use the {φk} to get a better predictor, φA.
SOUL: An Edge-Cloud System for Mobile Applications in a Sensor-Rich World
(2016)

With the Internet of Things, sensors are becoming ever more ubiquitous, but interacting with them continues to present numerous challenges, particularly for applications running on resource-constrained devices like smartphones. The SOUL abstractions in this paper address two issues faced by such applications: (1) access to sensors with the levels of convenience needed for their ubiquitous, dynamic use, and only by parties authorized to do so, and (2) scalability in sensor access, given today's multitude of sensors. Toward this end, SOUL, first, introduces a new abstraction for the applications to transparently and uniformly access both on-device and ambient sensors with associated actuators. Second, potentially expensive sensor-related processing needs not just occur on smartphones, but can also leverage edge-and remote-cloud resources. Finally, SOUL provides access control methods that permit users to easily define access permissions for sensors, which leverages users' social ties and captures the context in which access requests are made. SOUL demonstrates that the applications on Android platforms can scale the use of 100s of sensors with performance and energy efficiency.